Ideas and perspectives: Addressing environmental challenges using distributed data generation: From collaborative networks to artificial intelligence-enabled science

Abstract. Distributed data generation, or data collected from multiple sources and locations using standardized approaches and involving coordination among investigators, has emerged as a powerful approach to meet contemporary demands for scalable environmental knowledge. However, practitioners often lack guidance on best practices for distributed data generation, and a framework classifying its modalities is missing.

To address these gaps, we developed a conceptual framework organizing distributed data generation along two axes: participant-based (ranging from highly formalized to highly flexible) and method-based (from experimental to observational). This framework provides common vocabulary across modalities and describes how different approaches affect data generation logistics and outcomes. We propose operational best practices across three critical pillars: outreach, operations, and output (i.e., publications, data), leveraging lessons learned from over 35 existing distributed data projects. Lastly, we explore how emerging artificial intelligence (AI) capabilities may help address longstanding challenges in distributed data generation, including in coordination, adaptive sampling, and cross-project data integration. This perspective provides strategies and identifies opportunities to advance distributed data generation for addressing pressing biogeochemical, environmental, and societal challenges. We underscore the transformative potential of distributed data generation for modern, broad-scale environmental research, and provide guidance on how to realize that potential.